Conventionally, input of a keyword is received to identify and output from object data, another keyword that corresponds to the keyword. In a related technique, for example, if the number of corresponding documents is large when a document database is searched by a first search expression, one keyword is picked up from the first search expression and a co-occurrence database is searched to identify one or more narrowed-down candidate keywords. In this case, the co-occurrence database stores one keyword and a keyword co-occurring with the one keyword and the number of co-occurrences for a keyword extracted from all document files registered in the document database. For example, in an existing technique, after word division (morphological analysis) of text data, frequency detection eliminates repeats of words to create compressed text in which words are arranged in order of frequency. For examples of such techniques, refer to Japanese Laid-Open Patent Publication Nos. 2002-230037 and H6-348757.
Nonetheless, with the conventional techniques described above, if a related keyword of an input keyword is searched for (analyzed) by using a co-occurrence count and a keyword appearance count, a database of a sufficient size is prepared for sufficient analysis or for handling various input keywords. For example, as the data amount of object data increases in the case of big data etc., the size of the database also increases. On the other hand, if the size of the database is insufficient, since information of the co-occurrence count and the keyword appearance count is used in an incomplete range, the analysis becomes insufficient or only limited input keywords are handled.